Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various aspects of recommendation systems by some effective transfer techniques such as fine-tuning and prompt tuning, and so on. The crucial aspect of harnessing the power of language models in enhancing recommendation quality is the utilization of their high-quality representations of textual features and their extensive coverage of external knowledge to establish correlations between items and users. To provide a comprehensive understanding of the existing LLM-based recommendation systems, this survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec), with the latter being systematically sorted out for the first time. Furthermore, we systematically review and analyze existing LLM-based recommendation systems within each paradigm, providing insights into their methodologies, techniques, and performance. Additionally, we identify key challenges and several valuable findings to provide researchers and practitioners with inspiration.

翻译：大型语言模型（LLMs）已成为自然语言处理（NLP）领域的强大工具，近年来在推荐系统（RS）领域也获得了显著关注。这些模型通过自监督学习在海量数据上训练，在学习通用表征方面取得了显著成功，并有望通过微调、提示调优等有效迁移技术增强推荐系统的多个方面。利用语言模型提升推荐质量的关键在于运用其高质量的文本特征表征和广泛的外部知识覆盖范围，建立物品与用户之间的关联。为全面理解现有基于LLM的推荐系统，本文提出一种分类方法，将这些模型归为两大范式：判别式LLM推荐（DLLM4Rec）和生成式LLM推荐（GLLM4Rec），其中后者首次被系统梳理。我们进一步系统综述并分析了每种范式下现有的基于LLM的推荐系统，深入探讨其方法论、技术和性能表现。此外，我们识别了关键挑战并总结出若干有价值的研究发现，旨在为研究人员和实践者提供启示。